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Investigating the spatiotemporal control of lipopolysaccharide transport to the outer membrane in Escherichia coli
No full textThe Gram-negative cell envelope consists of an inner membrane (IM), an outer membrane (OM)
and a thin layer of peptidoglycan (PG) in between the two membranes. The IM has phospholipids
in its inner and outer leaflets, whereas the OM is asymmetric with phospholipids (PL) in the inner
leaflet and lipopolysaccharide (LPS) in the outer leaflet. The bacterial cell envelope is essential
for the cell, as it gives the bacteria its shape and acts as a protective barrier inhibiting the entry of
external antimicrobial factors such as antibiotics and phages. Many of the proteins involved in cell
envelope biogenesis are known, however it is less well understood how synthesis of the different
cell envelope components is coordinated.
Here we investigated the transport of LPS to the OM and how it is regulated during cell division.
First, we showed that fluorescent wheat germ agglutinin (WGA) binds specifically to N-acetylglucosamine (GlcNAc)-modified LPS (GlcNAcLPS) in the model organism E. coli MG1655. Previous
studies showed that WGA binds to GlcNAc, but this sugar is present in different molecules in the
cell envelope. The binding site for fluorescently labeled WGA (FL-WGA) on cells therefore remained
unclear. We showed that in intact cells, FL-WGA does not bind to PG as previously
thought but instead labels GlcNAcLPS. We used this discovery to develop an E. coli strain in which
transport of newly synthesized GlcNAcLPS to the OM could be tracked. Using this tool, we showed
that nascent LPS is inserted into the OM at dispersed locations in the cell cylinder during cell
elongation and at the division site during cell division. A similar pattern of labeling was previously
observed for protein insertion into the OM and for new PG synthesis, indicating that LPS transport
is likely to be coordinated with these processes.
While studying the effect of different cell division inhibitors on LPS transport at the division site,
we discovered that the division-specific PG synthesis inhibitor cephalexin blocks LPS transport at
midcell. Such a block was not observed when assembly of the division machinery (divisome) was
blocked in cephalexin treated cells by expression of the division inhibitor SulA. Thus, inhibition of
LPS transport at the division site in cephalexin treated cells, requires assembly of the divisome.
We hypothesized that PL is used to expand the OM at midcell when LPS transport is blocked by
cephalexin treatment. Accordingly, cephalexin treatment rendered cells sensitive to the detergent
sodium dodecyl sulfate (SDS) to which cells with an intact OM are normally resistant. These results
reveal that beta-lactam antibiotics like cephalexin not only affect PG synthesis but also disrupt
the permeability barrier of the OM. Additionally, these findings indicate that LPS transport
requires proper PG synthesis at the division site and that the two processes are likely to be coordinated
by a mechanism that remains to be elucidated.Biological and Biomedical Science
The Get List
No full textTV Producer Brooke Quinn escapes to a remote Alaskan fish camp. There, free from the professional pressure to stay unpregnant, she can pursue her next story. Or so she hopes. In a trade of stories with a fishwife, Brooke learns to salt salmon and what it takes to make it as an Outsider in the Alaskan Bush. A tenuous friendship builds as Brooke learns firsthand about the Alaska Native women gone missing in her fishing village, their names listed in an oilman’s yacht logbook and disguised as deckhands. The dark discovery convinces Brooke to extend her stay, immersing herself in a new language and people. Brooke wagers it all—her reputation, her marriage, her chance to start a family—to get the story that could make or break her career. But landing the logbook traced to the revered CEO of Arc’d, a major oil company celebrated for its green practices, could land her name on his “get list,” the women invited to his yacht who never return.
This creative work anchors me both in the culminating mission of my master’s–-publishing a novel—and my craft study: create intimacy with my audience through interiority. By focusing on the “inside voice” of the protagonist, Brooke Quinn, the craft challenge becomes constructing her interior world. The interiority must draw readers into a psychological escape so intoxicating that the emotional weight exchanged between the character and the reader genuinely resonates. To do this, I draw on plot-rich scenes from my professional experience: from covering an oil spill in the Gulf and dark money ties in Arctic Alaska to interviewing the CEO of the much-revered company Rivian ahead of its $5-billion electric car company being built in rural Georgia. As my muse Charlotte Brontë might say, Reader, I’m ready to write!Extension Studie
Smart Glasses to Monitor Intermittent Exotropia
No full textAcross the world, an estimated one percent of the population experiences intermittent exotropia, a condition in which one eye occasionally drifts outwards independent of the other eye, which often unknowingly occurs while tired, sick, or daydreaming. In this thesis, a pair of smart glasses are presented which allows for accurate and quantitative monitoring of the angle of deviation of both eyes. This pair of smart glasses preserves the look and feel of a conventional set of glasses, making efforts to minimize size and weight, but also enabling angular eye tracking. This solution is unique in that it takes advantage of edge computing to perform angular inferencing onboard the frames of the glasses themselves, allowing for an all-in-one solution that does not require being connected to a separate phone or computer for computational inferencing. This project also has possible future applications in virtual reality control, computer input, marketing research, and accessibility improvement.Engineering Sciences A
Discovery of a general mechanism for bacterial cell envelope polymer acylation
No full textBacteria frequently decorate their cell envelope polymers with acyl groups that regulate physiology, enhance virulence, and contribute to antibiotic resistance. How cells tackle the challenge of moving activated acyl groups from the cytoplasm—where they are made—onto extracytoplasmic polymers has been a longstanding question. In the work described in this thesis, I uncover a widespread strategy for bacterial cell envelope polymer acylation in which a membrane-bound O-acyltransferase (MBOAT) protein transfers acyl groups from intracellular thioester donors to the side-chain hydroxyl group of an extracytoplasmic tyrosine residue. The acylated tyrosine then serves as a donor for a separate transferase responsible for moving acyl groups to their next destination, usually the cell envelope polymer itself. In the pathway for D-alanylation of lipoteichoic acids, which is the primary focus of this thesis, the key extracytoplasmic tyrosine is located within a highly conserved six-amino acid motif at the C-terminus of a small membrane protein called DltX that holds the MBOAT protein and the other transferase together in a tripartite complex. For other pathways found in diverse bacteria and some archaea, the tyrosine is present in a similar six-amino motif at the C-terminus of the MBOAT protein itself. This work establishes that the function of the vast majority of bacterial MBOAT proteins is to produce acyl-tyrosine intermediates critical for cell envelope polymer modification, setting the stage for function-informed development of inhibitors that target these proteins.
In Chapter 1 of this thesis, I introduce bacterial cell envelope synthesis and modification as an important target for antimicrobial therapeutics. I also describe the history of the study of bacterial MBOAT-based cell envelope polymer acylation systems. In Chapter 2, I investigate the identity of the putative unknown intermediate in the long-studied lipoteichoic acid D-alanylation pathway, and I explain how I developed the hypothesis that a tyrosine-containing motif at the C-terminus of the small protein DltX is the key “missing piece” to that pathway’s mechanistic puzzle. I go on to describe how the work on DltX led me to propose that the specific mechanism I discovered is widespread across different polymer acylation pathways in diverse bacteria. Next, in Chapter 3, I collaborate to use in vitro biochemistry and structural biology approaches to provide solid experimental evidence for my proposed mechanism, again focusing primarily on the protein machinery responsible for lipoteichoic acid D-alanylation. Finally, I conclude in Chapter 4 with a discussion of interesting avenues for future exploration related to both the MBOAT-based acyl transfer pathways I explored in my thesis work and also acyl transfer pathways that use a different class of membrane-bound acyltransferases which I became interested in over the course of my studies.Chemical Biolog
Economic Security in Blockchain Systems
No full textA key scientific question underlying the blockchain ecosystem is to what extent the core
security properties of the protocols hold when assuming rational validators in the presence of
capable economic attackers. To what degree and at what cost can these systems be disrupted?
In this thesis, I analyze the underlying economic security properties of three of the most
fundamental decentralization consensus algorithms: proof of work (PoW), proof of stake (PoS),
and oracle information aggregation.
In Chapter 2 of this work, I counter a prominent narrative that PoW is inherently flawed in
an environment in which double-spend attacks are possible. By considering counterattacks, I
recover PoW robustness against reorganization attacks through a game-theoretic model. In
particular, I consider hashrate markets as a potential vector of attack and show that PoW
remains robust in this case.
In Chapter 3 of this work, I show novel chain reorganization and finality-delay attacks
on the PoS mechanism of Ethereum. These attacks are deviations from the ’honest’ staking
strategy, and I show that for participants staking a substantial percentage of the network’s
staked assets, these attacks can be cheap and destructive to the network.
In Chapter 4 of this work, I design an incentive mechanism for the information aggregation
of noisy signals that is highly resilient to bribery. I establish the asymptotic strength and
limitations of this mechanism against various classes of bribery including an attacker able to
condition bribes on individual reports and on the outcome of the information aggregation. I
achieve strong protection even in the latter case. To do this, I assume the presence of a source
of truth (SoT) that is prohibitively expensive for typical use but can be invoked infrequently.
This robustness to bribes is achieved even while in equilibrium there is no invocation of the
SoT.Engineering and Applied Sciences - Computer Scienc
Patterns of Place: Housing Supply, Racial Segregation, and the Suburbanization of Immigration
No full textPlace is central to understanding inequality. However, recent decades have seen significant shifts in the urban layout of the United States. In this dissertation, I address changes in housing and immigration through a mixed-methods approach combining causal inference, decomposition methods, and qualitative approaches. In particular, I analyze three recent spatial dynamics: declines in housing development, variation in racial integration, and the suburbanization of immigration.
Chapter Two examines Massachusetts’ Chapter 40B, a state law designed to override exclusionary zoning and expedite affordable housing projects, to analyze how informal opposition shapes housing outcomes. Housing affordability debates often focus on zoning regulations, but informal opposition through bargaining, delay, and deterrence also plays a critical role in restricting development. Using data collected from observations and public meeting records of 48 40B projects, I identify three key dynamics: density primacy, where opponents link concerns to project size; issue shifting, where objections evolve to sustain opposition; and policy learning, where opponents refine arguments to align with regulatory constraints. These strategies allow local actors to extract concessions and reshape developments even when formal mechanisms for denial are unavailable. Findings highlight the limitations of zoning reform alone in addressing housing shortages and underscore the need for policy solutions that mitigate procedural barriers to housing production. While public meetings are intended to facilitate community input, they often amplify opposition in ways that hinder new housing construction.
In Chapter Three, I investigate whether increasing housing development reduces Black-White residential segregation. Debates over housing supply, from NIMBY opposition to YIMBY advocacy, have focused largely on the economic consequences of permitting new housing, but their broader social effects remain underexplored. Using a panel of metropolitan areas from 1990 to 2020, I find that metros permitting more housing experienced larger declines in segregation. To strengthen causal claims, I employ an instrumental variable approach leveraging geologic constraints on buildability. I also show that within metro areas, towns permitting more housing saw greater reductions in segregation, reinforcing the link between housing supply and integration. These findings highlight the broader social consequences of housing development policies, suggesting that increasing housing supply may be a key lever for fostering racial equity.
In Chapter Four, I identify and question three assumptions about the suburbanization of immigration. The majority of immigrants in the U.S. now live in the suburbs of major metropolitan areas. Recent work on this phenomenon has focused on the outcomes of immigrant suburbanization, examining how immigrants encounter and make sense of different residential contexts. However, I argue that the literature has largely neglected to investigate the underlying process behind immigrant suburbanization. I analyze three assumptions related to this process: that moves to new destinations are driving suburbanization, that immigrants are suburbanizing more quickly than the U.S.-born, and that a suburb-specific mechanism is driving increases in immigrant-native neighborhood inequality. I use a series of decompositions to model each of these questions, finding evidence that largely cuts against these underlying assumptions in the field. In doing so, I show the value of situating spatial trends in immigration in the context of broader demographic trends and help clarify possible mechanisms driving immigrant suburbanization.Sociolog
Rigorous Loving: Black Women, Black Feminism, and Friendship, 1970-1996
No full text"Rigorous Loving: Black Women, Black Feminism, and Friendship, 1970-1996" is an interdisciplinary project that explores the multifaceted and mostly overlooked significance of Black women’s friendships to Black women’s cultural productions within the late 20th century United States. Applying methods from Black women’s history, Black feminist literary studies, transnational women of color feminist studies, and more, I unearth and analyze theories of friendship evident in the archival material, published interviews, and literary work of prominent activists, writers, and poets such as Audre Lorde, June Jordan, Alice Walker, Barbara Smith, and Lucille Clifton.
I return to these notable Black women political and literary figures to uplift what, I argue, is a neglected aspect (and influence on) their work: friendships. Friendships were significant to these writers’ lives and their cultural productions in a multitude of ways. First, my dissertation, reveals how friendships were vital to the existence and survival of many of their cultural productions. Second, through examining friendship beyond romanticized, heteronormative, and de-politicized framings, I reveal how these Black women writers theorized friendship (including the care and the conflict) as essential to their Black feminist praxis and as a site of political possibility that defied the normative expectations of friendship and Black womanhood within the Anglo-American patriarchal family. My dissertation centers four different cultural forms: retreats, letters, anthologies, and poetry. Each chapter is organized around one cultural production through which I investigate how friendships were manifested, deepened, or tested.
Ultimately, through this research, I propose that friendship appeared in at least three ways across these women’s work: (1) as a site of political theorizing, organizing, and knowledge production; (2) as a site of creative possibility and epistemological transgressions; and (3) as a space of refuge and survival. By “space of refuge,” I mean a spatial imaginary that is quite literally a “retreat,” outside of the domain of normative visions of cis-heterosexual womanhood, especially for Black women, Black feminists, and Black working-class lesbian feminists in a world that perpetually threatened their existence.American Studie
Characterization of a Novel Vesicular Protein of the Pancreatic Beta Cell
No full textType I diabetes results from the autoimmune destruction of pancreatic beta cells, leading to chronic hyperglycemia absent lifelong exogenous insulin treatment. Cell replacement therapy from stem cell derived beta cells offers a promising therapeutic avenue, by restoring endogenous insulin signaling, and a virtually unlimited supply of beta cells for basic science. It is unclear to what degree other beta cell proteins are involved in regulating glucose metabolism. We describe here ERseq08, identified in the lab by a novel sequencing technique, endoplasmic reticulum sequencing (ERseq), indicating the presence of its transcript in beta cells and positioned it as likely secreted from the same vesicles as is insulin. We examined the nature of the protein, determining that its expression pattern is limited to beta cells and other rare endocrine populations, identified the short isoform predominantly expressed, and observed increased expression in more functional beta cell populations. At the protein level, ERseq08 is present in insulin vesicles. We observe homozygous lethality in three separate loss of function alleles. Investigation of this phenotype revealed mild metabolic and secretory defects in the pancreas and liver of neonatal mutants. We also developed and explored a beta-cell specific overexpression allele but observed no significant changes to whole body metabolism with this expression system.Biological and Biomedical Science
On Statistical Learning for Structural Data: Data Fusion and Semi-supervised Learning
No full textNowadays, with the rise of the large data era, data tends to become more complex and structural. The data may incorporate various different sources, with distinct data quality, sample size, or set of covariates. For instance, in ecological inference and validation studies of epidemiology, it is common that the predictor and response of interest are gathered in different datasets. However, many statistical estimands of interest (e.g., in regression or causality) are functions of the joint distribution of multiple random variables. In this scenario, the only possible approach is one of data fusion, where multiple independent data sets, each measuring a subset of the random variables of interest, are combined for inference. In general, since all random variables are never observed jointly, their joint distribution, and hence also the estimand which is a function of it, is only partially identifiable. Unfortunately, the endpoints of the partially identifiable region depend in general on entire conditional distributions, rendering them hard both operationally and statistically to estimate. Inspired by this challenge, in the first chapter, we present a novel outer-bound on the region of partial identifiability (and establish conditions under which it is tight) that depends only on certain conditional first and second moments. This allows us to derive semiparametrically efficient estimators of our endpoint outer-bounds that only require the standard machine learning toolbox which learns conditional means. We prove asymptotic normality and semiparametric efficiency of our estimators and provide consistent estimators of their variances, enabling asymptotically valid confidence interval construction for our original partially identifiable estimand. We demonstrate the utility of our method in simulations and a data fusion problem from economics.
Beyond multi-source datasets, specialized formats-—such as network data-—are also a crucial component of structured data. With the large amount of networks and graphs in the modern data age, such as social networks (Facebook, LinkedIn, etc.), citation networks, and medical networks (e.g., propagation networks of treatment, infection networks of viruses), a proper theory for unveiling the underlying network sub-structures like communities becomes increasingly essential. Motivated by social network analysis and network-based recommendation systems, in the second chapter, we study a semi-supervised community detection problem in which the objective is to estimate the community label of a new node using the network topology and partially observed community labels of existing nodes. The network is modeled using a degree-corrected stochastic block model, which allows for severe degree heterogeneity and potentially non-assortative communities. We propose an algorithm that computes a `structural similarity metric' between the new node and each of the K communities by aggregating labeled and unlabeled data. The estimated label of the new node corresponds to the value of k that maximizes this similarity metric. Our method is fast and numerically outperforms existing semi-supervised algorithms. Theoretically, we derive explicit bounds for the misclassification error and show the efficiency of our method by comparing it with an ideal classifier. Our findings highlight, to the best of our knowledge, the first semi-supervised community detection algorithm that offers theoretical guarantees.
An extension of community detection is mixed membership estimation (MME), which is a classical problem in network data analysis. It extends community detection by allowing a node to have fractional memberships in multiple communities. One major challenge in mixed membership estimation is to discover the underlying simplex structure of the high-dimensional data, which corresponds to the membership of each individual. Previous literature mainly focuses on leveraging the data itself to identify the simplex in an unsupervised learning fashion. However, in many scenarios, prior information may be available. For instance, the past user history of certain individuals in a social network may provide a clue to their membership. To incorporate this class of knowledge, in the third chapter, we consider a semi-supervised setting where the membership vectors of a subset of nodes are given. This problem is significantly more challenging than semi-supervised community detection, and to the best of our knowledge, it has not been studied in most previous literature. We discover an insightful structural equation for utilizing the known labels, which inspires a delicate way of extending unsupervised mixed-membership estimation algorithms to the semi-supervised setting. Compared to unsupervised algorithms for identifying the vertex structure, our method does not require the existence of pure nodes and needs fewer regularity conditions on the vertices. Additionally, assuming a degree-corrected mixed membership model, we provide theoretical guarantees of our algorithm, and show that it is efficient in the sense that its error rate is the same as a least squares problem with more prior information on the vertices. To the best of our knowledge, this is the first semi-supervised vertex hunting algorithm with a theoretical guarantee. We also demonstrate the excellent performance of our algorithm in several empirical studies, illustrating that with only a tiny fraction of the label information, our method can dramatically outperform unsupervised algorithms.Statistic
From Cancer Initiation to Clinical Insight: Computational and Machine Learning Approaches to Tumor Dynamics and Precision Oncology
No full textDespite extensive research, many fundamental questions in cancer biology and clinical oncology remain unanswered, from the mechanisms underlying cancer initiation and early development to factors that influence diagnosis, treatment response, and patient outcomes. Computational modeling and machine learning frameworks provide powerful tools to address these challenges by enabling large scale mechanistic modeling and the integration of complex molecular and clinical information to uncover mechanisms, generate hypotheses, and guide clinical decisions. This thesis combines computational modeling of cancer initiation and machine learning frameworks for precision oncology to bridge fundamental and translational aspects of cancer research.
Studies of cancer initiation are impeded by complications of identifying and tracking the cell of origin. Recent work has shown that mutagen-induced DNA lesions can persist over multiple rounds of cell division, leaving a statistically interpretable footprint of cancer initiating events. Specifically, it allows estimation of the number of divisions between the DNA lesion introduction and the most recent common ancestor of the developed tumor (LAD). We developed a branching process model of cancer evolution following lesion introduction, and analyzed footprints of segregating lesions from previously published experimental mouse data and post-chemotherapy human metastatic tumors to obtain LAD estimates. We show that in all contexts cancer clones tended to start early, usually within 4 cell generations. Analytical and computational implementations of the branching process model suggested the fitness advantage of early cancer drivers must have exceeded 30% to achieve such early clone initiation.
At the clinical end of the spectrum, precision oncology has informed cancer care by enabling the discovery and application of diagnostic, prognostic, and/or predictive molecular biomarkers. However, many patients lack actionable biomarkers or fail to respond to biomarker-directed therapies. Patient similarity approaches can leverage comprehensive tumor profiling and prior clinical experiences from large cohorts for decision support, facilitating broader realization of precision oncology benefits. We developed a deep learning based modeling framework using real-world clinicogenomic data from a tertiary cancer center to (i) measure patient similarity based on embedded tumor genomic profiles and (ii) evaluate the association of derived patient subgroups and neighborhoods with shared therapeutic outcomes in a breast cancer specific and in a histology-agnostic pan-cancer setting. The model recovered clinically meaningful patient groups of both expected and previously unknown therapeutic associations, as well as patient-specific neighborhoods that could inform therapeutic trajectories more often than expected by chance in multiple clinical contexts. Moreover, model utility extended to patients without actionable genomic biomarkers and those with cancer of unknown primary (CUP) diagnoses, where neighborhoods aligned with independently predicted primary cancer type. These neighborhoods could also be examined over time in a continuously learning scenario.
Overall, these studies integrate fundamental models of cancer evolution and translational machine learning to develop approaches that advance cancer research from onset to clinical decision making. Our branching process model allowed inference of tumor initiation and growth parameters based on events preceding the most recent common ancestor of the initiating clone as opposed to characteristics of fully grown tumors. In parallel, our similarity-based modeling framework distilled complex molecular and clinical data into concise, context-specific insights that augment rather than replace clinician judgment, providing a foundation for real-time learning, patient-centered decision support in precision oncology.Biomedical Informatic